Impacto de la pandemia (Covid-19) en el Proceso Enseñanza - Aprendizaje en Alumnos del CU UAEM Tenancingo
Bibliographic record
Abstract
Undoubtedly, an unexpected event with a global impact that had a significant impact on the teaching-learning process was the covid-19 pandemic, because this process involves interactions between students and teachers, in such a way that teaching is planned according to the curriculum, based on needs identified through evaluation and is realized through the training of teachers. However, it became necessary to provide such online learning units in order to comply with health protocols and avoid possible contagion through agglomeration. The objective of this project was to identify what were the problems faced by students in the face of the change of classroom classes to virtual or online. The research question was whether online teaching strategies during the first quarter of the pandemic produced favorable results in the teaching-learning process. The most outstanding results relate to the preference of students for the impartation of classroom classes, as well as regarding the emergent strategies implemented, which were rated by students as unfavorable. Finally, as a conclusion of this project, we can say that the emerging online alternative used momentarily served to continue the delivery of the programs of the learning units during the period of confinement caused by covid-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".